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import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import librosa
import soundfile
from sklearn import preprocessing
import os
import random
import re
from uyghur import uyghur_latin
featurelen = 128
sample_rate = 22050
fft_len = 1024
window_len = fft_len
window = "hann"
white_noise,_=librosa.load('white.wav',sr=sample_rate, duration=15.0)
perlin_noise,_=librosa.load('perlin.wav',sr=sample_rate, duration=15.0)
cafe_noise, _ = librosa.load('cafe.wav',sr=sample_rate, duration=15.0)
radio_noise, _ = librosa.load('radionoise.wav',sr=sample_rate, duration=15.0)
def addnoise(audio):
rnd = random.random()
if len(audio) > len(white_noise):
pass
elif rnd <0.25:
audio = audio + white_noise[:len(audio)]
elif rnd <0.50:
audio = audio + perlin_noise[:audio.shape[0]]
elif rnd <0.75:
audio = audio + radio_noise[:audio.shape[0]]
else:
audio = audio + cafe_noise[:audio.shape[0]]
return audio
def randomstretch(audio):
factor = random.uniform(0.8, 1.2)
audio = librosa.core.resample(audio,sample_rate,sample_rate*factor)
return audio
def spec_augment(feat, T=50, F=13, time_mask_num=1, freq_mask_num=1):
#def spec_augment(feat, T=70, F=15, time_mask_num=1, freq_mask_num=1):
rnd = random.random()
feat_size = feat.size(0)
seq_len = feat.size(1)
if rnd< 0.33:
# time mask
for _ in range(time_mask_num):
t = random.randint(0, T)
t0 = random.randint(0, seq_len - t)
feat[:, t0 : t0 + t] = 0
elif rnd <0.66:
# freq mask
for _ in range(freq_mask_num):
f = random.randint(0, F)
f0 = random.randint(0, feat_size - f)
feat[f0 : f0 + f, :] = 0
else:
# time mask
for _ in range(time_mask_num):
t = random.randint(0, T)
t0 = random.randint(0, seq_len - t)
feat[:, t0 : t0 + t] = 0
# freq mask
for _ in range(freq_mask_num):
f = random.randint(0, F)
f0 = random.randint(0, feat_size - f)
feat[f0 : f0 + f, :] = 0
return feat
def melfuture(wav_path, augument = False):
audio, s_r = librosa.load(wav_path, sr=sample_rate, res_type='polyphase')
if augument:
if random.random()<0.5:
audio = randomstretch(audio)
if random.random()<0.5:
audio = addnoise(audio)
audio = preprocessing.minmax_scale(audio, axis=0)
audio = librosa.effects.preemphasis(audio)
hop_len = 200
if augument and random.random()<0.5:
hop_len = random.randint(160,240)
spec = librosa.feature.melspectrogram(y=audio, sr=s_r, n_fft=fft_len, hop_length=hop_len, n_mels=featurelen, fmax=8000)
spec = librosa.power_to_db(spec)
spec = (spec - spec.mean()) / spec.std()
spec = torch.FloatTensor(spec)
if augument == True and random.random()<0.5:
spec = spec_augment(spec)
return spec
def rawfuture(wav_path, augument = False):
audio, s_r = librosa.load(wav_path, sr=sample_rate, res_type='polyphase')
audio = preprocessing.minmax_scale(audio, axis=0)
if augument:
if random.random()<0.5:
audio = addnoise(audio)
if random.random()<0.5:
audio = randomstretch(audio)
audio = librosa.effects.preemphasis(audio)
spec = torch.FloatTensor(audio)
spec.unsqueeze_(0)
spec = (spec - spec.mean()) / spec.std()
return spec
class SpeechDataset(Dataset):
def __init__(self, index_path, augumentation = False):
self.Raw = False
with open(index_path,encoding='utf_8_sig') as f:
lines = f.readlines()
self.idx = []
for x in lines:
item = x.strip().split("\t")
line = []
line.append(item[0])
char_indx = uyghur_latin.encode(item[1])
line.append(char_indx)
self.idx.append(line)
self.augument = augumentation
def __getitem__(self, index):
wav_path, char_index = self.idx[index]
if self.Raw == True:
x = rawfuture(wav_path, self.augument)
else:
x = melfuture(wav_path, self.augument)
return x, char_index, wav_path
def __len__(self):
return len(self.idx)
def _collate_fn(batch):
batch = sorted(batch, key=lambda sample: sample[0].size(1), reverse=True)
input_lens = [sample[0].size(1) for sample in batch]
target_lens = [len(sample[1]) for sample in batch]
inputs = torch.zeros(len(batch), batch[0][0].size(0), max(input_lens) ,dtype=torch.float32)
targets = torch.zeros(len(batch), max(target_lens),dtype=torch.long).fill_(uyghur_latin.pad_idx)
target_lens = torch.IntTensor(target_lens)
input_lens = torch.IntTensor(input_lens)
paths = []
for x, sample in enumerate(batch):
tensor = sample[0]
target = sample[1]
seq_length = tensor.size(1)
inputs[x].narrow(1, 0, seq_length).copy_(tensor)
targets[x][:len(target)] = torch.LongTensor(target)
paths.append(sample[2])
return inputs, targets, input_lens, target_lens, paths
class SpeechDataLoader(DataLoader):
def __init__(self, *args, **kwargs):
"""
Creates a data loader for AudioDatasets.
"""
super(SpeechDataLoader, self).__init__(*args, **kwargs)
self.collate_fn = _collate_fn
# The following code is from: http://hetland.org/coding/python/levenshtein.py
def levenshtein(a,b):
"Calculates the Levenshtein distance between a and b."
n, m = len(a), len(b)
if n > m:
# Make sure n <= m, to use O(min(n,m)) space
a,b = b,a
n,m = m,n
current = list(range(n+1))
for i in range(1,m+1):
previous, current = current, [i]+[0]*n
for j in range(1,n+1):
add, delete = previous[j]+1, current[j-1]+1
change = previous[j-1]
if a[j-1] != b[i-1]:
change = change + 1
current[j] = min(add, delete, change)
return current[n]
def wer(s1, src):
sw = src.split()
return levenshtein(s1.split(),sw), len(sw)
def cer(s1, src):
return levenshtein(s1,src),len(src)
def cer_wer(preds, targets):
err_c, lettercnt, err_w, wordcnt = 0,0,0,0
for pred, target in zip(preds, targets):
c_er, c_cnt = cer(pred, target)
w_er, w_cnt = wer(pred, target)
err_c += c_er
lettercnt += c_cnt
wordcnt += w_cnt
err_w += w_er
return err_c, lettercnt, err_w, wordcnt
def random_speed():
y, s_r = librosa.load("test1.wav", sr=sample_rate, res_type='polyphase')
factor = random.uniform(0.8, 1.2)
new_sr = s_r*factor
new_y = librosa.core.resample(y,s_r,new_sr)
soundfile.write("test1_1.wav",new_y, s_r)
audio = librosa.effects.time_stretch(y,factor)
soundfile.write("test1_2.wav",audio, s_r)
def sinaq():
new_y, s_r = librosa.load("test1.wav", sr=sample_rate, res_type='polyphase')
new_y = addnoise(new_y)
#new_y = librosa.effects.preemphasis(new_y)
new_y = preprocessing.minmax_scale(new_y, axis=0)
soundfile.write("test1_1.wav",new_y, s_r)
new_y, s_r = librosa.load("test2.wav", sr=sample_rate, res_type='polyphase')
new_y = preprocessing.minmax_scale(new_y, axis=0)
new_y = addnoise(new_y)
#new_y = librosa.effects.preemphasis(new_y)
soundfile.write("test2_1.wav",new_y, s_r)
new_y, s_r = librosa.load("test3.wav", sr=sample_rate, res_type='polyphase')
new_y = preprocessing.minmax_scale(new_y, axis=0)
new_y = addnoise(new_y)
#new_y = librosa.effects.preemphasis(new_y)
soundfile.write("test3_1.wav",new_y, s_r)
new_y, s_r = librosa.load("test4.wav", sr=sample_rate, res_type='polyphase')
new_y = preprocessing.minmax_scale(new_y, axis=0)
new_y = addnoise(new_y)
#new_y = librosa.effects.preemphasis(new_y)
soundfile.write("test4_1.wav",new_y, s_r)
new_y, s_r = librosa.load("test6.wav", sr=sample_rate, res_type='polyphase')
new_y = preprocessing.minmax_scale(new_y, axis=0)
new_y = addnoise(new_y)
#new_y = librosa.effects.preemphasis(new_y)
soundfile.write("test6_1.wav",new_y, s_r)
if __name__ == "__main__":
#import matplotlib.pyplot as plt
#import librosa.display
#random_speed()
sinaq()
#y, s_r = librosa.load("test6.wav", sr=sample_rate, res_type='polyphase')
#soundfile.write("test6_1.wav",addnoise(y), s_r)
#soundfile.write("test6_2.wav",addnoise(y), s_r)
#soundfile.write("test6_3.wav",addnoise(y), s_r)
#soundfile.write("test6_4.wav",addnoise(y), s_r)
#soundfile.write("test6_5.wav",addnoise(y), s_r)